In the last few years several academic works have tested different simulation tools in order to understand the cause behind the rupture of aneurysms. However, these tools have limited or no impact on patients’ treatment. Due to this, the present work aims at developing a rule-based virtual automated process to aid Abdominal Aorta Aneurysm (AAA) diagnosis, which is useful in consequent treatment.

Computational Fluid Dynamics (CFD) is the main tool employed to support the decision making process and the final therapy. So, CFD modeling is the main part of the entire workflow, which starts from medical images (DICOM) and ends with the 3D visualization of AAA rupture risk. For CFD simulations, CAD anatomy reconstruction of the AAA is required. To the purpose, the DICOM data is read, properly segmented and exported to obtain the CFD-ready CAD model. The entire process employs knowledge-based rules.

Concerning the next step of CFD simulation, the latter are necessary to implement a correct physics and numerical setup, too. In particular, the mesh study considers a simplified geometric model of arteries and the aneurysm, and analyses are done to study the effects of 2D and 3D grid topology, the influence of grid refinement, the presence of prismatic boundary layer and finally the fluid viscosity. Moreover, the influence of fully developed boundary conditions can be considered.

In addition to the analyses parameters mentioned above, the influence of the time step size, the number of inner iterations per time step, typology of numerical schemes and fluid rheology are also considered. The post-processing and its visualization make numerical data presentable in a portable format, which is easily understandable, since it allows an immediate visual analysis of the velocity gradients, pressure and WSS, as well as characteristic variables which indicate possible development of local vortices and helical flows. An automated procedure is able to perform the time integration of transient CFD data, compute hemodynamic indices and generate relative the 3D render visualization.

An overview and further development of this workflow in HPC and cloud computing environment are presented. Findings, application and validation on a real patient specific case show the feasibility of the entire workflow in an embedded mode. Rules extracted from the research activity are at the base of the embedded algorithms that drive the entire workflow from DICOM PACS to 3D visualization.